Overview

Dataset statistics

Number of variables24
Number of observations744
Missing cells650
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory139.6 KiB
Average record size in memory192.2 B

Variable types

Categorical9
DateTime1
Numeric14

Alerts

name has constant value ""Constant
preciptype has constant value ""Constant
snow has constant value ""Constant
snowdepth has constant value ""Constant
severerisk is highly imbalanced (97.3%)Imbalance
stations is highly imbalanced (84.4%)Imbalance
preciptype has 650 (87.4%) missing valuesMissing
datetime has unique valuesUnique
precip has 650 (87.4%) zerosZeros
cloudcover has 34 (4.6%) zerosZeros
solarradiation has 460 (61.8%) zerosZeros
solarenergy has 483 (64.9%) zerosZeros
uvindex has 545 (73.3%) zerosZeros

Reproduction

Analysis started2024-04-13 05:51:29.640066
Analysis finished2024-04-13 05:52:11.329293
Duration41.69 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

name
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
New York City,USA
744 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters12648
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York City,USA
2nd rowNew York City,USA
3rd rowNew York City,USA
4th rowNew York City,USA
5th rowNew York City,USA

Common Values

ValueCountFrequency (%)
New York City,USA 744
100.0%

Length

2024-04-13T01:52:11.479465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T01:52:11.631981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
new 744
33.3%
york 744
33.3%
city,usa 744
33.3%

Most occurring characters

ValueCountFrequency (%)
1488
 
11.8%
N 744
 
5.9%
e 744
 
5.9%
w 744
 
5.9%
Y 744
 
5.9%
o 744
 
5.9%
r 744
 
5.9%
k 744
 
5.9%
C 744
 
5.9%
i 744
 
5.9%
Other values (6) 4464
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1488
 
11.8%
N 744
 
5.9%
e 744
 
5.9%
w 744
 
5.9%
Y 744
 
5.9%
o 744
 
5.9%
r 744
 
5.9%
k 744
 
5.9%
C 744
 
5.9%
i 744
 
5.9%
Other values (6) 4464
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1488
 
11.8%
N 744
 
5.9%
e 744
 
5.9%
w 744
 
5.9%
Y 744
 
5.9%
o 744
 
5.9%
r 744
 
5.9%
k 744
 
5.9%
C 744
 
5.9%
i 744
 
5.9%
Other values (6) 4464
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1488
 
11.8%
N 744
 
5.9%
e 744
 
5.9%
w 744
 
5.9%
Y 744
 
5.9%
o 744
 
5.9%
r 744
 
5.9%
k 744
 
5.9%
C 744
 
5.9%
i 744
 
5.9%
Other values (6) 4464
35.3%

datetime
Date

UNIQUE 

Distinct744
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Minimum2023-12-01 00:00:00
Maximum2023-12-31 23:00:00
2024-04-13T01:52:11.812871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:12.091802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temp
Real number (ℝ)

Distinct83
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.030914
Minimum-2.2
Maximum15.7
Zeros6
Zeros (%)0.8%
Negative10
Negative (%)1.3%
Memory size5.9 KiB
2024-04-13T01:52:12.306610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.2
5-th percentile1.6
Q14.4
median6.8
Q38.9
95-th percentile13.4
Maximum15.7
Range17.9
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.3799004
Coefficient of variation (CV)0.48071992
Kurtosis-0.084476299
Mean7.030914
Median Absolute Deviation (MAD)2.1
Skewness0.16040455
Sum5231
Variance11.423727
MonotonicityNot monotonic
2024-04-13T01:52:12.569270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 37
 
5.0%
7.2 34
 
4.6%
8.4 32
 
4.3%
5.6 28
 
3.8%
5 28
 
3.8%
7.9 27
 
3.6%
6.1 27
 
3.6%
8.9 24
 
3.2%
9.4 23
 
3.1%
8.8 22
 
3.0%
Other values (73) 462
62.1%
ValueCountFrequency (%)
-2.2 1
 
0.1%
-1.8 1
 
0.1%
-1.7 2
 
0.3%
-1.3 1
 
0.1%
-1.2 1
 
0.1%
-0.7 2
 
0.3%
-0.1 2
 
0.3%
0 6
0.8%
0.6 3
0.4%
1 2
 
0.3%
ValueCountFrequency (%)
15.7 1
 
0.1%
15.6 1
 
0.1%
15.5 1
 
0.1%
15.1 6
0.8%
15 5
0.7%
14.4 4
0.5%
14.3 3
 
0.4%
13.9 8
1.1%
13.8 4
0.5%
13.4 6
0.8%

feelslike
Real number (ℝ)

Distinct143
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3283602
Minimum-6.7
Maximum15.7
Zeros6
Zeros (%)0.8%
Negative73
Negative (%)9.8%
Memory size5.9 KiB
2024-04-13T01:52:12.796865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-6.7
5-th percentile-1.1
Q12.4
median5.35
Q37.325
95-th percentile13.4
Maximum15.7
Range22.4
Interquartile range (IQR)4.925

Descriptive statistics

Standard deviation4.2602851
Coefficient of variation (CV)0.799549
Kurtosis-0.31395247
Mean5.3283602
Median Absolute Deviation (MAD)2.55
Skewness0.27868748
Sum3964.3
Variance18.150029
MonotonicityNot monotonic
2024-04-13T01:52:13.246969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8 17
 
2.3%
6.8 17
 
2.3%
7.2 16
 
2.2%
5.9 15
 
2.0%
6.7 14
 
1.9%
10.7 14
 
1.9%
6 13
 
1.7%
11.7 12
 
1.6%
10.6 12
 
1.6%
8.4 12
 
1.6%
Other values (133) 602
80.9%
ValueCountFrequency (%)
-6.7 1
 
0.1%
-5 1
 
0.1%
-4.5 1
 
0.1%
-4 1
 
0.1%
-3.7 1
 
0.1%
-3.2 1
 
0.1%
-3.1 1
 
0.1%
-2.9 2
0.3%
-2.8 2
0.3%
-2.7 3
0.4%
ValueCountFrequency (%)
15.7 1
 
0.1%
15.6 1
 
0.1%
15.5 1
 
0.1%
15.1 6
0.8%
15 5
0.7%
14.4 4
0.5%
14.3 3
 
0.4%
13.9 8
1.1%
13.8 4
0.5%
13.4 6
0.8%

dew
Real number (ℝ)

Distinct133
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4446237
Minimum-11.4
Maximum14.4
Zeros2
Zeros (%)0.3%
Negative335
Negative (%)45.0%
Memory size5.9 KiB
2024-04-13T01:52:13.462798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-11.4
5-th percentile-7.3
Q1-3.4
median1.1
Q36.2
95-th percentile9.985
Maximum14.4
Range25.8
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation5.7408853
Coefficient of variation (CV)3.973966
Kurtosis-0.94493472
Mean1.4446237
Median Absolute Deviation (MAD)5
Skewness0.041366326
Sum1074.8
Variance32.957764
MonotonicityNot monotonic
2024-04-13T01:52:13.744992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1 24
 
3.2%
8.8 23
 
3.1%
6.7 22
 
3.0%
7.8 21
 
2.8%
5 21
 
2.8%
1.1 20
 
2.7%
5.6 18
 
2.4%
-2.9 18
 
2.4%
7.2 18
 
2.4%
-3.4 17
 
2.3%
Other values (123) 542
72.8%
ValueCountFrequency (%)
-11.4 1
 
0.1%
-10.9 1
 
0.1%
-10.7 1
 
0.1%
-10.6 1
 
0.1%
-10.2 1
 
0.1%
-10.1 2
0.3%
-9.3 1
 
0.1%
-9.1 2
0.3%
-9 3
0.4%
-8.9 1
 
0.1%
ValueCountFrequency (%)
14.4 1
 
0.1%
13.9 6
0.8%
13.8 2
 
0.3%
13.4 4
0.5%
13.3 1
 
0.1%
12.9 3
0.4%
12.8 1
 
0.1%
12.3 5
0.7%
12.2 1
 
0.1%
11.8 1
 
0.1%

humidity
Real number (ℝ)

Distinct647
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.404624
Minimum30.19
Maximum96.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:14.013765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum30.19
5-th percentile42.5
Q155.6425
median69.19
Q385.9525
95-th percentile92.84
Maximum96.5
Range66.31
Interquartile range (IQR)30.31

Descriptive statistics

Standard deviation16.965709
Coefficient of variation (CV)0.24444638
Kurtosis-1.1377864
Mean69.404624
Median Absolute Deviation (MAD)15.27
Skewness-0.12351269
Sum51637.04
Variance287.83528
MonotonicityNot monotonic
2024-04-13T01:52:14.258752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.36 4
 
0.5%
88.82 4
 
0.5%
89.27 3
 
0.4%
93.43 3
 
0.4%
92.59 3
 
0.4%
92.2 3
 
0.4%
92.49 3
 
0.4%
61.93 3
 
0.4%
65.07 3
 
0.4%
89.33 3
 
0.4%
Other values (637) 712
95.7%
ValueCountFrequency (%)
30.19 1
0.1%
30.52 1
0.1%
31.52 1
0.1%
32.11 1
0.1%
32.33 1
0.1%
32.76 1
0.1%
33.5 1
0.1%
33.6 1
0.1%
34.01 1
0.1%
34.51 1
0.1%
ValueCountFrequency (%)
96.5 1
0.1%
96.47 2
0.3%
96.39 1
0.1%
96.14 1
0.1%
93.69 1
0.1%
93.68 1
0.1%
93.66 1
0.1%
93.56 1
0.1%
93.52 1
0.1%
93.47 1
0.1%

precip
Real number (ℝ)

ZEROS 

Distinct66
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012908602
Minimum0
Maximum0.773
Zeros650
Zeros (%)87.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:14.562889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.06185
Maximum0.773
Range0.773
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.061204595
Coefficient of variation (CV)4.7413805
Kurtosis68.747683
Mean0.012908602
Median Absolute Deviation (MAD)0
Skewness7.5080775
Sum9.604
Variance0.0037460024
MonotonicityNot monotonic
2024-04-13T01:52:14.809900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 650
87.4%
0.014 6
 
0.8%
0.019 5
 
0.7%
0.008 4
 
0.5%
0.016 3
 
0.4%
0.062 3
 
0.4%
0.011 3
 
0.4%
0.026 3
 
0.4%
0.006 3
 
0.4%
0.013 2
 
0.3%
Other values (56) 62
 
8.3%
ValueCountFrequency (%)
0 650
87.4%
0.005 2
 
0.3%
0.006 3
 
0.4%
0.008 4
 
0.5%
0.011 3
 
0.4%
0.013 2
 
0.3%
0.014 6
 
0.8%
0.015 1
 
0.1%
0.016 3
 
0.4%
0.018 1
 
0.1%
ValueCountFrequency (%)
0.773 1
0.1%
0.66 1
0.1%
0.607 1
0.1%
0.396 1
0.1%
0.391 1
0.1%
0.364 1
0.1%
0.339 1
0.1%
0.334 1
0.1%
0.29 1
0.1%
0.276 1
0.1%

precipprob
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
650 
100
94 

Length

Max length3
Median length1
Mean length1.2526882
Min length1

Characters and Unicode

Total characters932
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 650
87.4%
100 94
 
12.6%

Length

2024-04-13T01:52:15.085668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T01:52:15.305021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 650
87.4%
100 94
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 838
89.9%
1 94
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 838
89.9%
1 94
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 838
89.9%
1 94
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 838
89.9%
1 94
 
10.1%

preciptype
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.1%
Missing650
Missing (%)87.4%
Memory size5.9 KiB
rain
94 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters376
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrain
2nd rowrain
3rd rowrain
4th rowrain
5th rowrain

Common Values

ValueCountFrequency (%)
rain 94
 
12.6%
(Missing) 650
87.4%

Length

2024-04-13T01:52:15.479634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T01:52:15.630209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
rain 94
100.0%

Most occurring characters

ValueCountFrequency (%)
r 94
25.0%
a 94
25.0%
i 94
25.0%
n 94
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 376
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 94
25.0%
a 94
25.0%
i 94
25.0%
n 94
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 376
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 94
25.0%
a 94
25.0%
i 94
25.0%
n 94
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 376
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 94
25.0%
a 94
25.0%
i 94
25.0%
n 94
25.0%

snow
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
744 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters744
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 744
100.0%

Length

2024-04-13T01:52:15.798985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T01:52:15.963583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 744
100.0%

Most occurring characters

ValueCountFrequency (%)
0 744
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 744
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 744
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 744
100.0%

snowdepth
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
744 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters744
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 744
100.0%

Length

2024-04-13T01:52:16.220131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T01:52:16.538858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 744
100.0%

Most occurring characters

ValueCountFrequency (%)
0 744
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 744
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 744
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 744
100.0%

windgust
Real number (ℝ)

Distinct126
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.186022
Minimum5.4
Maximum92.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:16.892720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5.4
5-th percentile7.6
Q113
median24.1
Q333.5
95-th percentile60.97
Maximum92.5
Range87.1
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation16.194334
Coefficient of variation (CV)0.61843429
Kurtosis1.0884323
Mean26.186022
Median Absolute Deviation (MAD)9.85
Skewness1.1442315
Sum19482.4
Variance262.25644
MonotonicityNot monotonic
2024-04-13T01:52:17.360294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 68
 
9.1%
11.2 55
 
7.4%
16.6 49
 
6.6%
27.7 47
 
6.3%
14.8 41
 
5.5%
25.9 40
 
5.4%
7.6 39
 
5.2%
29.5 37
 
5.0%
18.4 26
 
3.5%
13 25
 
3.4%
Other values (116) 317
42.6%
ValueCountFrequency (%)
5.4 11
 
1.5%
7.6 39
5.2%
9.4 68
9.1%
11.2 55
7.4%
13 25
 
3.4%
14.8 41
5.5%
16.6 49
6.6%
18.4 26
 
3.5%
20.5 21
 
2.8%
22.3 20
 
2.7%
ValueCountFrequency (%)
92.5 1
0.1%
82.3 1
0.1%
78.6 1
0.1%
77.8 1
0.1%
76.2 1
0.1%
76 2
0.3%
73.6 1
0.1%
73 1
0.1%
72.4 1
0.1%
72.2 2
0.3%

windspeed
Real number (ℝ)

Distinct174
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.898253
Minimum0
Maximum37.9
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:17.771690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q17.2
median9.95
Q314.5
95-th percentile21.9
Maximum37.9
Range37.9
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation6.4266927
Coefficient of variation (CV)0.58969937
Kurtosis0.6444692
Mean10.898253
Median Absolute Deviation (MAD)3.85
Skewness0.58553504
Sum8108.3
Variance41.302379
MonotonicityNot monotonic
2024-04-13T01:52:18.256303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 18
 
2.4%
7.6 18
 
2.4%
11.2 18
 
2.4%
9.2 17
 
2.3%
7.2 17
 
2.3%
5.2 16
 
2.2%
11.1 15
 
2.0%
0.4 15
 
2.0%
7.5 14
 
1.9%
7.8 12
 
1.6%
Other values (164) 584
78.5%
ValueCountFrequency (%)
0 1
 
0.1%
0.1 18
2.4%
0.2 10
1.3%
0.3 3
 
0.4%
0.4 15
2.0%
0.5 10
1.3%
0.7 8
1.1%
0.8 3
 
0.4%
0.9 6
 
0.8%
1 3
 
0.4%
ValueCountFrequency (%)
37.9 1
0.1%
34.5 1
0.1%
32.7 1
0.1%
32.4 1
0.1%
31.4 1
0.1%
31 1
0.1%
29.2 1
0.1%
29 1
0.1%
27.7 1
0.1%
27.5 1
0.1%

winddir
Real number (ℝ)

Distinct186
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.65188
Minimum0
Maximum360
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:18.490294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q152
median250
Q3271
95-th percentile350
Maximum360
Range360
Interquartile range (IQR)219

Descriptive statistics

Standard deviation117.19396
Coefficient of variation (CV)0.62452855
Kurtosis-1.4823486
Mean187.65188
Median Absolute Deviation (MAD)60
Skewness-0.36821604
Sum139613
Variance13734.424
MonotonicityNot monotonic
2024-04-13T01:52:18.773515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
260 34
 
4.6%
270 21
 
2.8%
261 20
 
2.7%
250 17
 
2.3%
50 16
 
2.2%
59 15
 
2.0%
259 15
 
2.0%
269 14
 
1.9%
41 13
 
1.7%
271 13
 
1.7%
Other values (176) 566
76.1%
ValueCountFrequency (%)
0 4
 
0.5%
1 6
0.8%
2 10
1.3%
3 2
 
0.3%
4 7
0.9%
5 7
0.9%
6 6
0.8%
7 6
0.8%
8 2
 
0.3%
9 2
 
0.3%
ValueCountFrequency (%)
360 7
0.9%
359 7
0.9%
358 3
0.4%
357 2
 
0.3%
356 2
 
0.3%
355 2
 
0.3%
354 3
0.4%
353 5
0.7%
352 2
 
0.3%
351 2
 
0.3%

sealevelpressure
Real number (ℝ)

Distinct291
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1019.9276
Minimum984.3
Maximum1039.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:19.157387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum984.3
5-th percentile1003.615
Q11012.95
median1020.95
Q31029.825
95-th percentile1033.185
Maximum1039.9
Range55.6
Interquartile range (IQR)16.875

Descriptive statistics

Standard deviation10.805135
Coefficient of variation (CV)0.010594022
Kurtosis-0.089171101
Mean1019.9276
Median Absolute Deviation (MAD)8.75
Skewness-0.58892246
Sum758826.1
Variance116.75094
MonotonicityNot monotonic
2024-04-13T01:52:19.758194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1030 12
 
1.6%
1030.4 11
 
1.5%
1016.4 9
 
1.2%
1016.8 9
 
1.2%
1005.2 8
 
1.1%
1027.7 8
 
1.1%
1030.1 8
 
1.1%
1031.3 8
 
1.1%
1015.9 7
 
0.9%
1016.9 7
 
0.9%
Other values (281) 657
88.3%
ValueCountFrequency (%)
984.3 1
0.1%
984.4 1
0.1%
984.8 1
0.1%
985.1 1
0.1%
985.3 1
0.1%
986.5 1
0.1%
986.8 1
0.1%
987.5 1
0.1%
988.6 1
0.1%
989.1 1
0.1%
ValueCountFrequency (%)
1039.9 1
0.1%
1039.8 1
0.1%
1039.7 1
0.1%
1039.3 1
0.1%
1039.2 1
0.1%
1038.6 1
0.1%
1038.5 1
0.1%
1037.7 1
0.1%
1037.1 2
0.3%
1036.9 1
0.1%

cloudcover
Real number (ℝ)

ZEROS 

Distinct75
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.752688
Minimum0
Maximum100
Zeros34
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:20.179192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q10.8
median84.6
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)99.2

Descriptive statistics

Standard deviation46.470757
Coefficient of variation (CV)0.84873928
Kurtosis-1.8725598
Mean54.752688
Median Absolute Deviation (MAD)15.4
Skewness-0.18583079
Sum40736
Variance2159.5312
MonotonicityNot monotonic
2024-04-13T01:52:20.596074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 243
32.7%
0.4 116
15.6%
99.6 64
 
8.6%
1.5 56
 
7.5%
0.8 52
 
7.0%
0 34
 
4.6%
98.6 17
 
2.3%
88.1 14
 
1.9%
28.7 9
 
1.2%
2.7 8
 
1.1%
Other values (65) 131
17.6%
ValueCountFrequency (%)
0 34
 
4.6%
0.4 116
15.6%
0.5 3
 
0.4%
0.8 52
7.0%
0.9 3
 
0.4%
1.2 2
 
0.3%
1.5 56
7.5%
1.7 4
 
0.5%
2.1 2
 
0.3%
2.7 8
 
1.1%
ValueCountFrequency (%)
100 243
32.7%
99.9 3
 
0.4%
99.6 64
 
8.6%
99.3 1
 
0.1%
99.2 1
 
0.1%
99 2
 
0.3%
98.9 1
 
0.1%
98.6 17
 
2.3%
98.3 1
 
0.1%
97.3 1
 
0.1%

visibility
Real number (ℝ)

Distinct58
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.539651
Minimum2.9
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:20.894704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile6
Q115.7
median16
Q316
95-th percentile16
Maximum16
Range13.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation3.1205178
Coefficient of variation (CV)0.21462124
Kurtosis3.1498968
Mean14.539651
Median Absolute Deviation (MAD)0
Skewness-2.1114336
Sum10817.5
Variance9.7376316
MonotonicityNot monotonic
2024-04-13T01:52:21.218004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 491
66.0%
15.9 45
 
6.0%
15.8 17
 
2.3%
5.9 12
 
1.6%
9.7 11
 
1.5%
14.1 10
 
1.3%
15.7 10
 
1.3%
15.6 9
 
1.2%
7.9 8
 
1.1%
6 7
 
0.9%
Other values (48) 124
 
16.7%
ValueCountFrequency (%)
2.9 1
 
0.1%
3 2
 
0.3%
3.9 1
 
0.1%
4 2
 
0.3%
4.1 1
 
0.1%
4.8 2
 
0.3%
4.9 6
0.8%
5 5
0.7%
5.8 4
 
0.5%
5.9 12
1.6%
ValueCountFrequency (%)
16 491
66.0%
15.9 45
 
6.0%
15.8 17
 
2.3%
15.7 10
 
1.3%
15.6 9
 
1.2%
15.5 7
 
0.9%
15.4 4
 
0.5%
15.3 3
 
0.4%
15.1 1
 
0.1%
14.1 10
 
1.3%

solarradiation
Real number (ℝ)

ZEROS 

Distinct170
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.255376
Minimum0
Maximum596
Zeros460
Zeros (%)61.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:21.472795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q360
95-th percentile410.25
Maximum596
Range596
Interquartile range (IQR)60

Descriptive statistics

Standard deviation124.30142
Coefficient of variation (CV)1.9966374
Kurtosis4.6160188
Mean62.255376
Median Absolute Deviation (MAD)0
Skewness2.3317365
Sum46318
Variance15450.842
MonotonicityNot monotonic
2024-04-13T01:52:21.718223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 460
61.8%
18 6
 
0.8%
53 6
 
0.8%
19 6
 
0.8%
84 6
 
0.8%
9 6
 
0.8%
118 5
 
0.7%
5 5
 
0.7%
7 5
 
0.7%
23 5
 
0.7%
Other values (160) 234
31.5%
ValueCountFrequency (%)
0 460
61.8%
3 2
 
0.3%
5 5
 
0.7%
6 1
 
0.1%
7 5
 
0.7%
8 1
 
0.1%
9 6
 
0.8%
11 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
ValueCountFrequency (%)
596 1
0.1%
566 1
0.1%
547 1
0.1%
543 1
0.1%
529 1
0.1%
527 1
0.1%
497 1
0.1%
496 1
0.1%
494 1
0.1%
489 1
0.1%

solarenergy
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22432796
Minimum0
Maximum2.1
Zeros483
Zeros (%)64.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:21.903893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile1.5
Maximum2.1
Range2.1
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.44764926
Coefficient of variation (CV)1.9955126
Kurtosis4.6042409
Mean0.22432796
Median Absolute Deviation (MAD)0
Skewness2.3285843
Sum166.9
Variance0.20038986
MonotonicityNot monotonic
2024-04-13T01:52:22.104076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 483
64.9%
0.1 47
 
6.3%
0.2 35
 
4.7%
0.4 29
 
3.9%
0.3 27
 
3.6%
0.5 17
 
2.3%
0.6 15
 
2.0%
0.7 15
 
2.0%
1.6 11
 
1.5%
1.7 10
 
1.3%
Other values (12) 55
 
7.4%
ValueCountFrequency (%)
0 483
64.9%
0.1 47
 
6.3%
0.2 35
 
4.7%
0.3 27
 
3.6%
0.4 29
 
3.9%
0.5 17
 
2.3%
0.6 15
 
2.0%
0.7 15
 
2.0%
0.8 4
 
0.5%
0.9 5
 
0.7%
ValueCountFrequency (%)
2.1 1
 
0.1%
2 3
 
0.4%
1.9 2
 
0.3%
1.8 4
 
0.5%
1.7 10
1.3%
1.6 11
1.5%
1.5 9
1.2%
1.4 6
0.8%
1.3 4
 
0.5%
1.2 8
1.1%

uvindex
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61021505
Minimum0
Maximum6
Zeros545
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-13T01:52:22.360209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2660609
Coefficient of variation (CV)2.0747783
Kurtosis4.57957
Mean0.61021505
Median Absolute Deviation (MAD)0
Skewness2.3146494
Sum454
Variance1.6029103
MonotonicityNot monotonic
2024-04-13T01:52:22.543004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 545
73.3%
1 90
 
12.1%
2 40
 
5.4%
4 25
 
3.4%
5 23
 
3.1%
3 19
 
2.6%
6 2
 
0.3%
ValueCountFrequency (%)
0 545
73.3%
1 90
 
12.1%
2 40
 
5.4%
3 19
 
2.6%
4 25
 
3.4%
5 23
 
3.1%
6 2
 
0.3%
ValueCountFrequency (%)
6 2
 
0.3%
5 23
 
3.1%
4 25
 
3.4%
3 19
 
2.6%
2 40
 
5.4%
1 90
 
12.1%
0 545
73.3%

severerisk
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
10
742 
3
 
2

Length

Max length2
Median length2
Mean length1.9973118
Min length1

Characters and Unicode

Total characters1486
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 742
99.7%
3 2
 
0.3%

Length

2024-04-13T01:52:22.737459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T01:52:22.950189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
10 742
99.7%
3 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 742
49.9%
0 742
49.9%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 742
49.9%
0 742
49.9%
3 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 742
49.9%
0 742
49.9%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 742
49.9%
0 742
49.9%
3 2
 
0.1%

conditions
Categorical

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Clear
292 
Overcast
247 
Partially cloudy
111 
Rain, Overcast
92 
Rain
 
1

Length

Max length22
Median length16
Mean length8.7715054
Min length4

Characters and Unicode

Total characters6526
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st rowClear
2nd rowClear
3rd rowClear
4th rowClear
5th rowClear

Common Values

ValueCountFrequency (%)
Clear 292
39.2%
Overcast 247
33.2%
Partially cloudy 111
 
14.9%
Rain, Overcast 92
 
12.4%
Rain 1
 
0.1%
Rain, Partially cloudy 1
 
0.1%

Length

2024-04-13T01:52:23.189690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T01:52:23.417121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
overcast 339
35.7%
clear 292
30.8%
partially 112
 
11.8%
cloudy 112
 
11.8%
rain 94
 
9.9%

Most occurring characters

ValueCountFrequency (%)
a 949
14.5%
r 743
11.4%
e 631
9.7%
l 628
9.6%
c 451
 
6.9%
t 451
 
6.9%
O 339
 
5.2%
v 339
 
5.2%
s 339
 
5.2%
C 292
 
4.5%
Other values (10) 1364
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6526
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 949
14.5%
r 743
11.4%
e 631
9.7%
l 628
9.6%
c 451
 
6.9%
t 451
 
6.9%
O 339
 
5.2%
v 339
 
5.2%
s 339
 
5.2%
C 292
 
4.5%
Other values (10) 1364
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6526
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 949
14.5%
r 743
11.4%
e 631
9.7%
l 628
9.6%
c 451
 
6.9%
t 451
 
6.9%
O 339
 
5.2%
v 339
 
5.2%
s 339
 
5.2%
C 292
 
4.5%
Other values (10) 1364
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6526
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 949
14.5%
r 743
11.4%
e 631
9.7%
l 628
9.6%
c 451
 
6.9%
t 451
 
6.9%
O 339
 
5.2%
v 339
 
5.2%
s 339
 
5.2%
C 292
 
4.5%
Other values (10) 1364
20.9%

icon
Categorical

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
cloudy
247 
clear-night
192 
clear-day
100 
rain
94 
partly-cloudy-night
77 

Length

Max length19
Median length17
Mean length9.2889785
Min length4

Characters and Unicode

Total characters6911
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclear-night
2nd rowclear-night
3rd rowclear-night
4th rowclear-night
5th rowclear-night

Common Values

ValueCountFrequency (%)
cloudy 247
33.2%
clear-night 192
25.8%
clear-day 100
13.4%
rain 94
 
12.6%
partly-cloudy-night 77
 
10.3%
partly-cloudy-day 34
 
4.6%

Length

2024-04-13T01:52:23.661727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T01:52:23.893416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
cloudy 247
33.2%
clear-night 192
25.8%
clear-day 100
13.4%
rain 94
 
12.6%
partly-cloudy-night 77
 
10.3%
partly-cloudy-day 34
 
4.6%

Most occurring characters

ValueCountFrequency (%)
l 761
11.0%
c 650
 
9.4%
a 631
 
9.1%
y 603
 
8.7%
- 514
 
7.4%
r 497
 
7.2%
d 492
 
7.1%
t 380
 
5.5%
n 363
 
5.3%
i 363
 
5.3%
Other values (6) 1657
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 761
11.0%
c 650
 
9.4%
a 631
 
9.1%
y 603
 
8.7%
- 514
 
7.4%
r 497
 
7.2%
d 492
 
7.1%
t 380
 
5.5%
n 363
 
5.3%
i 363
 
5.3%
Other values (6) 1657
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 761
11.0%
c 650
 
9.4%
a 631
 
9.1%
y 603
 
8.7%
- 514
 
7.4%
r 497
 
7.2%
d 492
 
7.1%
t 380
 
5.5%
n 363
 
5.3%
i 363
 
5.3%
Other values (6) 1657
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 761
11.0%
c 650
 
9.4%
a 631
 
9.1%
y 603
 
8.7%
- 514
 
7.4%
r 497
 
7.2%
d 492
 
7.1%
t 380
 
5.5%
n 363
 
5.3%
i 363
 
5.3%
Other values (6) 1657
24.0%

stations
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
696 
72505394728,72055399999,KLGA,KJRB,F1417,KNYC
 
33
72505394728,72055399999,KLGA,KJRB,F1417,72503014732
 
9
72505394728,KLGA,KJRB,F1417,KNYC,72503014732
 
3
72505394728,KLGA,KJRB,F1417,KNYC
 
1
Other values (2)
 
2

Length

Max length56
Median length56
Mean length55.287634
Min length32

Characters and Unicode

Total characters41134
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
2nd row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
3rd row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
4th row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
5th row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732

Common Values

ValueCountFrequency (%)
72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732 696
93.5%
72505394728,72055399999,KLGA,KJRB,F1417,KNYC 33
 
4.4%
72505394728,72055399999,KLGA,KJRB,F1417,72503014732 9
 
1.2%
72505394728,KLGA,KJRB,F1417,KNYC,72503014732 3
 
0.4%
72505394728,KLGA,KJRB,F1417,KNYC 1
 
0.1%
72505394728,KLGA,F1417,KNYC,72503014732 1
 
0.1%
72055399999,KLGA,KJRB,F1417,KNYC,72503014732 1
 
0.1%

Length

2024-04-13T01:52:24.193488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T01:52:24.436945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
72505394728,72055399999,klga,kjrb,f1417,knyc,72503014732 696
93.5%
72505394728,72055399999,klga,kjrb,f1417,knyc 33
 
4.4%
72505394728,72055399999,klga,kjrb,f1417,72503014732 9
 
1.2%
72505394728,klga,kjrb,f1417,knyc,72503014732 3
 
0.4%
72505394728,klga,kjrb,f1417,knyc 1
 
0.1%
72505394728,klga,f1417,knyc,72503014732 1
 
0.1%
72055399999,klga,kjrb,f1417,knyc,72503014732 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
9 4438
10.8%
, 4414
10.7%
7 4389
10.7%
5 3674
8.9%
2 3645
8.9%
0 2902
 
7.1%
3 2902
 
7.1%
K 2222
 
5.4%
1 2198
 
5.3%
4 2197
 
5.3%
Other values (11) 8153
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 4438
10.8%
, 4414
10.7%
7 4389
10.7%
5 3674
8.9%
2 3645
8.9%
0 2902
 
7.1%
3 2902
 
7.1%
K 2222
 
5.4%
1 2198
 
5.3%
4 2197
 
5.3%
Other values (11) 8153
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 4438
10.8%
, 4414
10.7%
7 4389
10.7%
5 3674
8.9%
2 3645
8.9%
0 2902
 
7.1%
3 2902
 
7.1%
K 2222
 
5.4%
1 2198
 
5.3%
4 2197
 
5.3%
Other values (11) 8153
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 4438
10.8%
, 4414
10.7%
7 4389
10.7%
5 3674
8.9%
2 3645
8.9%
0 2902
 
7.1%
3 2902
 
7.1%
K 2222
 
5.4%
1 2198
 
5.3%
4 2197
 
5.3%
Other values (11) 8153
19.8%

Interactions

2024-04-13T01:52:08.276460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:30.074859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:32.609302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:34.868256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:37.189825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:39.550666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:42.029615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:46.055874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:50.869202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:55.148064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:57.697017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:00.705278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:03.117276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:05.532694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:08.458390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:30.258346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:32.783490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:35.029955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:37.358326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:39.722787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:42.229122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:46.308892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:51.388490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:55.328281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:58.143575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:00.895420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:03.273663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:05.701721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:08.598492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:30.443526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:32.972550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:35.168327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:37.502907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:39.878249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:42.394849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:46.602921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:51.797997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:55.506424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:58.308590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:01.065633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:03.417068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:05.850368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:08.734817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:30.616155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:33.151666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:35.318576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:37.647219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:40.052762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:42.747614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:46.803800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:52.203981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:55.678557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:58.468321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:01.261718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:03.591680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:06.040338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:08.896054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:30.801419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:33.348422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:35.497538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:37.820288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:40.239457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:43.160190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:47.036340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:52.600679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:55.850055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:58.617547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:01.445424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:03.755928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:06.250052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:09.088434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:31.051667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:33.521501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:35.677296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:38.024620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:40.441919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:43.479394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:47.256117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:52.915303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:56.048255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:58.817245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:01.635279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:03.939432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:06.442903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:09.222537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:31.210309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:33.660471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:35.824959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:38.175211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:40.576167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:43.793692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:47.474978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:53.200213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:56.195037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:58.969404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:01.802862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:04.103919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:06.610302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:09.385073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:31.376159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:33.833599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:36.150041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:38.347559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:40.734751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:44.165242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:47.721540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:53.481631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:56.365983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:59.189222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:01.991949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:04.279129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:06.980160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:09.543061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:31.538424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:33.991975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:36.291925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:38.543220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:40.907248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:44.359215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:48.008749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:53.773702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:56.523513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:59.382886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:02.169888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:04.472699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:07.173684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:09.697237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:31.689550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:34.141580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:36.436468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:38.724015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:41.085633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:44.603447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:48.219388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:54.009446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:56.823216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:59.548551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:02.341652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:04.646068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:07.365164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:09.873204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:31.847121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:34.277471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:36.578301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:38.899091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:41.289468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:45.090171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:48.530763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:54.248330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:57.033455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:59.813341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:02.494931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:04.806104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:07.550422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:10.065769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:32.010206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:34.428395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:36.718301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:39.061750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:41.443954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:45.370688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:49.085132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:54.471853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:57.214020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:00.145346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:02.658071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:04.986886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:07.712814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:10.231772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:32.183371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:34.586125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:36.876567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:39.213722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:41.641501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:45.604663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:49.606300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:54.657222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:57.380116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:00.350154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:02.812576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:05.183520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:07.910336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:10.401571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:32.376589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:34.733654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:37.038370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:39.398478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:41.820187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:45.814774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:50.329445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:54.876872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:51:57.540641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:00.537816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:02.955355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:05.360770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T01:52:08.106380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Missing values

2024-04-13T01:52:10.649615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T01:52:11.128473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

namedatetimetempfeelslikedewhumidityprecipprecipprobpreciptypesnowsnowdepthwindgustwindspeedwinddirsealevelpressurecloudcovervisibilitysolarradiationsolarenergyuvindexsevereriskconditionsiconstations
0New York City,USA2023-12-01T00:00:007.95.31.061.820.00NaN0020.514.72591021.60.416.000.0010Clearclear-night72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
1New York City,USA2023-12-01T01:00:006.74.81.066.860.00NaN0016.69.42191021.10.816.000.0010Clearclear-night72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
2New York City,USA2023-12-01T02:00:006.74.00.564.680.00NaN0016.614.32491021.70.816.000.0010Clearclear-night72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
3New York City,USA2023-12-01T03:00:006.24.60.567.180.00NaN0014.87.72591021.60.816.000.0010Clearclear-night72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
4New York City,USA2023-12-01T04:00:006.24.60.667.290.00NaN0014.87.62201021.60.416.000.0010Clearclear-night72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
5New York City,USA2023-12-01T05:00:005.73.31.071.940.00NaN0016.611.12391021.70.416.000.0010Clearclear-night72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
6New York City,USA2023-12-01T06:00:005.73.61.172.220.00NaN0014.89.42391022.00.416.000.0010Clearclear-night72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
7New York City,USA2023-12-01T07:00:005.73.01.172.130.00NaN0014.812.82691022.00.416.090.0010Clearclear-night72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
8New York City,USA2023-12-01T08:00:005.73.71.675.060.00NaN0014.89.02621022.30.816.0910.3110Clearclear-day72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
9New York City,USA2023-12-01T09:00:006.24.72.174.830.00NaN0068.47.52001022.31.516.02300.8210Clearclear-day72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
namedatetimetempfeelslikedewhumidityprecipprecipprobpreciptypesnowsnowdepthwindgustwindspeedwinddirsealevelpressurecloudcovervisibilitysolarradiationsolarenergyuvindexsevereriskconditionsiconstations
734New York City,USA2023-12-31T14:00:006.22.8-2.952.470.00NaN0031.718.22701014.799.616.0650.2110Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,72503014732
735New York City,USA2023-12-31T15:00:006.23.3-2.952.320.00NaN0022.314.52701015.0100.016.0400.1010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
736New York City,USA2023-12-31T16:00:006.13.8-2.952.390.00NaN0018.411.22681015.1100.016.050.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
737New York City,USA2023-12-31T17:00:006.24.2-3.450.550.00NaN0014.89.42881015.7100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
738New York City,USA2023-12-31T18:00:006.23.8-3.450.520.00NaN0014.811.12631016.0100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
739New York City,USA2023-12-31T19:00:006.23.9-3.450.520.00NaN0014.810.62731016.4100.015.900.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
740New York City,USA2023-12-31T20:00:005.73.2-2.356.510.00NaN0014.811.22601016.4100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
741New York City,USA2023-12-31T21:00:005.73.7-1.858.850.00NaN0013.09.02631016.4100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
742New York City,USA2023-12-31T22:00:005.73.7-1.360.990.00NaN0013.09.22501016.4100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
743New York City,USA2023-12-31T23:00:005.73.6-0.863.120.00NaN0014.89.42591016.499.616.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732